AbstentionBench - underspecified context - GPQA-Diamond - Precision: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - GPQA-Diamond - Precision as Abstention Precision (%); values converted from 0-1 to percent. Precision and recall must be read together: always abstaining is not necessarily useful. Scenario/dataset pairs and SFT/DPO/RL checkpoints remain separate; no synthetic overall average. Source: facebookresearch/AbstentionBench analysis/abstention_performance.csv.

Metric: Abstention Precision (%). Source: github.com. Saturation forecast: Estimated already saturated. 21 models tracked.

Top models

#ModelScoreOverall rank
1GPT-4o100#357
2Llama 3.1 8B Instruct100#1017
3Llama 3.1 405B Instruct100#470
4Qwen 2.5 32B Instruct100#509
5O1100#190
6Llama-3.1-Tulu-3-8B100#848
7Llama-3.1-Tulu-3-8B-DPO100#992
8Llama-3.1-Tulu-3-70B96.3#499
9Llama-3.1-Tulu-3-70B-DPO96.3#732
10Gemini 1.5 Pro95.24#454
11Llama 3.1 70B Instruct94.74#567
12Llama 3.3 70B Instruct93.75#541
13O1 (High)92.31#190 (O1)
14Llama 3.1 70B90#588
15Llama 3.1 8B87.5#1130

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=abstentionbench-underspecified-context-gpqa-diamond-precision · How It Works · Data refreshed daily, snapshot 2026-11-05.